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1259 lines
3.1 MiB
1259 lines
3.1 MiB
2 years ago
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import scipy.stats as sts\n",
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"import pickle"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"execfile('Stimulus.py')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_folder = \"Data/\"\n",
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"\n",
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"stim_file = \"Stiminfo_PVCre_2021_0012_s06_e14.csv\"\n",
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"stim = pd.read_csv(data_folder+stim_file)\n",
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"\n",
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"spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n",
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"spike_times = np.load(data_folder+spike_times_file, allow_pickle=True)\n",
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"active = [len(spike_times[i]) > 0 for i in range(len(spike_times))]\n",
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"spike_times = spike_times[np.where(active)]\n",
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"\n",
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"num_unit = len(spike_times)\n",
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"num_trial = len(stim)\n",
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"\n",
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"# sort by firing rate\n",
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"\n",
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"num_spike = list(map(len, spike_times))\n",
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"# num_spike = np.array([len(spike_times[i]) for i in range(len(spike_times))])\n",
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"spike_times = spike_times[np.argsort(num_spike)[::-1]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"stim_data.pkl\n",
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"dict_keys(['stim_val', 'trial_stim_id', 'key_list', 'num_trial', 'trial_pair_id', 'pair_val', 'pair_trial_id', 'stim_id_trial', 'num_stim'])\n",
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"spike_data.pkl\n",
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"dict_keys(['spike_count_rate', 'avg_firing_rate', 'sem_firing_rate', 'firing_rate', 'stim_num_trial', 'C_r_fphi_theta', 'theta_hist', 'phase_hist', 'pair_hist'])\n",
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"corr_data.pkl\n",
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"dict_keys(['corr_stim_unit', 'optimal_avg_firing_rate', 'stim_hist', 'stim_hist_caution'])\n"
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]
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}
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],
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"source": [
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"execfile('load.py')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Spike count rate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"spike_count_rate = np.zeros((num_unit, num_trial))\n",
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"for trial_id in range(num_trial):\n",
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" t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
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" for unit_id in range(num_unit):\n",
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" spike_count_rate[unit_id, trial_id] = np.sum((spike_times[unit_id] < t_off) & (spike_times[unit_id] > t_on))\n",
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" \n",
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" spike_count_rate[:, trial_id] /= t_off-t_on"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Judging by PSTH, try counting the spikes only towards the end of each trial. Each trial is 84 ms long and we count the spikes over (50, 90) for each trial."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"spike_count_rate = np.zeros((num_unit, num_trial))\n",
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"for trial_id in range(num_trial):\n",
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" t_on, t_off = stim['stim_ontime'][trial_id]+0.050, stim['stim_offtime'][trial_id]+0.084 - 0.084\n",
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" for unit_id in range(num_unit):\n",
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" spike_count_rate[unit_id, trial_id] = np.sum((spike_times[unit_id] < t_off) & (spike_times[unit_id] > t_on))\n",
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" \n",
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" spike_count_rate[:, trial_id] /= t_off-t_on"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Average firing rate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"avg_firing_rate = {}\n",
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"sem_firing_rate = {}\n",
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"for key in stim_id_trial:\n",
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" avg_firing_rate[key] = np.array([np.mean(spike_count_rate[:, stim_id_trial[key][i]], axis=1) for i in range(num_stim[key])]).T\n",
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" sem_firing_rate[key] = np.array([np.std(spike_count_rate[:, stim_id_trial[key][i]], axis=1)/np.sqrt(len(stim_id_trial[key][i])) for i in range(num_stim[key])]).T\n",
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"\n",
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"# for each stim pair, gives the population activity averaged over 50 trials (transpose)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# mean_firing_rate[unit_id, orientation_id, phase_id]\n",
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"avg_firing_rate_pair = np.zeros((num_unit, num_stim['orientation'], num_stim['phase']))\n",
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"for i in range(num_stim['pair']):\n",
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" avg_firing_rate_pair[:, i//20, i%20] = np.mean(spike_count_rate[:, stim_id_trial['pair'][i]], axis=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Firing rate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# concatenate spike times of all trials with the same stimuli\n",
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"# firing rate\n",
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"trial_spike_times = np.ndarray((num_unit, num_trial), dtype=object)\n",
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"delta_t_on = 0.02 # 20ms\n",
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"delta_t_off = 0.2\n",
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"for trial_id in range(num_trial):\n",
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" t_on, t_off = stim['stim_ontime'][trial_id]-delta_t_on, stim['stim_offtime'][trial_id]+delta_t_off\n",
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" for unit_id in range(num_unit):\n",
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" trial_spike_times[unit_id, trial_id] = spike_times[unit_id][np.where((spike_times[unit_id] < t_off) & (spike_times[unit_id] > t_on))] - t_on\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"max_dur = max(stim['stim_offtime']-stim['stim_ontime'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"time_bin = 0.001 # 1ms\n",
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"T = delta_t_on + delta_t_off + max_dur\n",
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"time_bin_edges = np.arange(0, T+time_bin, time_bin)\n",
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"num_bins = len(time_bin_edges)-1\n",
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"\n",
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"firing_rate = {}\n",
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"\n",
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"for key in key_list:\n",
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" firing_rate[key] = np.zeros((num_unit, num_stim[key], num_bins))\n",
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" for stim_id, trials in enumerate(stim_id_trial[key]):\n",
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" for unit_id in range(num_unit):\n",
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" cumulative_spike_times = np.concatenate(trial_spike_times[unit_id, trials])\n",
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" for t in cumulative_spike_times:\n",
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" firing_rate[key][unit_id, stim_id, int(t//time_bin)] += 1\n",
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" # firing_rate[key][unit_id, stim_id] = np.histogram(cumulative_spike_times, time_bin_edges)\n",
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" \n",
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" firing_rate[key] /= time_bin*len(trials)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"stim_num_trial = {'pair':50, 'orientation':1000, 'phase':1000}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"time_bin = 0.001 # 1ms\n",
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"delta_t_on = 0.02 # 20ms\n",
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"delta_t_off = 0.2\n",
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"max_dur = max(stim['stim_offtime']-stim['stim_ontime'])\n",
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"\n",
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"T = delta_t_on + delta_t_off + max_dur\n",
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"time_bin_edges = np.arange(0, T+time_bin, time_bin)\n",
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"num_bins = len(time_bin_edges) - 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"latest_spike_time = max([np.max(s) for s in spike_times if len(s)])\n",
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"latest_stim_offtime = list(stim['stim_offtime'])[-1]\n",
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"experiment_dur = max([latest_spike_time, latest_stim_offtime])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ipywidgets as widgets # interactive display"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "xmin must be a single scalar value, but got g--",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32mc:\\Users\\arash\\Documents\\WiSe2022\\Research Project (Topology)\\staticgrating-phasefreq-topology\\01_Firing_Rate.ipynb Cell 20'\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/WiSe2022/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000019?line=8'>9</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(time, firing_rate[key][unit_id, stim_id])\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/WiSe2022/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000019?line=9'>10</a>\u001b[0m plt\u001b[39m.\u001b[39maxhline(avg_firing_rate[key][unit_id, stim_id])\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/WiSe2022/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000019?line=10'>11</a>\u001b[0m plt\u001b[39m.\u001b[39;49maxhline(np\u001b[39m.\u001b[39;49mmean(avg_firing_rate[\u001b[39m'\u001b[39;49m\u001b[39mpair\u001b[39;49m\u001b[39m'\u001b[39;49m][unit_id]), \u001b[39m'\u001b[39;49m\u001b[39mg--\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/WiSe2022/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000019?line=11'>12</a>\u001b[0m plt\u001b[39m.\u001b[39maxhline(\u001b[39mlen\u001b[39m(spike_times[unit_id])\u001b[39m/\u001b[39mexperiment_dur, \u001b[39m'\u001b[39m\u001b[39mb--\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/WiSe2022/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000019?line=13'>14</a>\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mpair\u001b[39m\u001b[39m'\u001b[39m:\n",
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"File \u001b[1;32mc:\\Users\\arash\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\matplotlib\\pyplot.py:2349\u001b[0m, in \u001b[0;36maxhline\u001b[1;34m(y, xmin, xmax, **kwargs)\u001b[0m\n\u001b[0;32m 2347\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39maxhline)\n\u001b[0;32m 2348\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39maxhline\u001b[39m(y\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, xmin\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, xmax\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m-> 2349\u001b[0m \u001b[39mreturn\u001b[39;00m gca()\u001b[39m.\u001b[39;49maxhline(y\u001b[39m=\u001b[39;49my, xmin\u001b[39m=\u001b[39;49mxmin, xmax\u001b[39m=\u001b[39;49mxmax, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
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"File \u001b[1;32mc:\\Users\\arash\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\matplotlib\\axes\\_axes.py:726\u001b[0m, in \u001b[0;36mAxes.axhline\u001b[1;34m(self, y, xmin, xmax, **kwargs)\u001b[0m\n\u001b[0;32m 675\u001b[0m \u001b[39m@docstring\u001b[39m\u001b[39m.\u001b[39mdedent_interpd\n\u001b[0;32m 676\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39maxhline\u001b[39m(\u001b[39mself\u001b[39m, y\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, xmin\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, xmax\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m 677\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 678\u001b[0m \u001b[39m Add a horizontal line across the axis.\u001b[39;00m\n\u001b[0;32m 679\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 724\u001b[0m \u001b[39m >>> axhline(y=.5, xmin=0.25, xmax=0.75)\u001b[39;00m\n\u001b[0;32m 725\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 726\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_check_no_units([xmin, xmax], [\u001b[39m'\u001b[39;49m\u001b[39mxmin\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mxmax\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[0;32m 727\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mtransform\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m kwargs:\n\u001b[0;32m 728\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mtransform\u001b[39m\u001b[39m'\u001b[39m\u001b[39m is not allowed as a keyword \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 729\u001b[0m \u001b[39m\"\u001b[39m\u001b[39margument; axhline generates its own transform.\u001b[39m\u001b[39m\"\u001b[39m)\n",
|
||
|
|
"File \u001b[1;32mc:\\Users\\arash\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\matplotlib\\axes\\_axes.py:814\u001b[0m, in \u001b[0;36mAxes._check_no_units\u001b[1;34m(vals, names)\u001b[0m\n\u001b[0;32m 812\u001b[0m \u001b[39mfor\u001b[39;00m val, name \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(vals, names):\n\u001b[0;32m 813\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m munits\u001b[39m.\u001b[39m_is_natively_supported(val):\n\u001b[1;32m--> 814\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m}\u001b[39;00m\u001b[39m must be a single scalar value, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 815\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbut got \u001b[39m\u001b[39m{\u001b[39;00mval\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
|
||
|
|
"\u001b[1;31mValueError\u001b[0m: xmin must be a single scalar value, but got g--"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# PSTH\n",
|
||
|
|
"\n",
|
||
|
|
"unit_id = 1\n",
|
||
|
|
"key = 'orientation'\n",
|
||
|
|
"stim_id = 4\n",
|
||
|
|
"\n",
|
||
|
|
"time = (time_bin_edges[:-1] - delta_t_on)/time_bin\n",
|
||
|
|
"stim_time = np.arange(0, max_dur, time_bin)/time_bin\n",
|
||
|
|
"plt.plot(time, firing_rate[key][unit_id, stim_id])\n",
|
||
|
|
"plt.axhline(avg_firing_rate[key][unit_id, stim_id])\n",
|
||
|
|
"plt.axhline(np.mean(avg_firing_rate['pair'][unit_id]), 'g--')\n",
|
||
|
|
"plt.axhline(len(spike_times[unit_id])/experiment_dur, 'b--')\n",
|
||
|
|
"\n",
|
||
|
|
"if key == 'pair':\n",
|
||
|
|
" plt.hlines(stim_time, -1*np.ones(len(stim_time)), 'r-', label=' $\\\\theta = %d' %stim_val[key][stim_id][0]+'^\\circ$\\n $\\phi = %d' %stim_val[key][stim_id][1]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
"else:\n",
|
||
|
|
" plt.plot(stim_time, -1*np.ones(len(stim_time)), 'r-', label='stimulus: '+key+' = $%d' %stim_val[key][stim_id]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.ylabel('firing rate/Hz')\n",
|
||
|
|
"plt.xlabel('t/ms')\n",
|
||
|
|
"plt.suptitle('PSTH ' + '%d'%stim_num_trial[key] + ' trials - unit '+'%d'%unit_id)\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 83,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# sliding window\n",
|
||
|
|
"n = 80\n",
|
||
|
|
"kernel = np.ones(n)/n\n",
|
||
|
|
"smooth_firing_rate = {}\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" smooth_firing_rate[key] = np.zeros((num_unit, num_stim[key], num_bins))\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" for stim_id in range(num_stim[key]):\n",
|
||
|
|
" smooth_firing_rate[key][unit_id, stim_id] = np.convolve(firing_rate[key][unit_id, stim_id], kernel)[int(n//2):int(n//2)+num_bins]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 84,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"200.0"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 84,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"max([np.max(firing_rate[key]) for key in key_list])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 92,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "5a1e3d6fdb96409781ab702eb7fd0a49",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(RadioButtons(description='stimulus:', options=('pair', 'orientation', 'phase'), value='p…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# key = 'orientation'\n",
|
||
|
|
"time = (time_bin_edges[:-1] - delta_t_on)/time_bin\n",
|
||
|
|
"stim_time = np.arange(0, max_dur, time_bin)/time_bin\n",
|
||
|
|
"\n",
|
||
|
|
"# @widgets.interact(stim_id=widgets.FloatSlider(0, min=0, max=1, step=0.01),\n",
|
||
|
|
"# key=widgets.RadioButtons(\n",
|
||
|
|
"# options=key_list,\n",
|
||
|
|
"# # value='pineapple', # Defaults to 'pineapple'\n",
|
||
|
|
"# # layout={'width': 'max-content'}, # If the items' names are long\n",
|
||
|
|
"# description='stimulus:',\n",
|
||
|
|
"# disabled=False\n",
|
||
|
|
"# ))\n",
|
||
|
|
"\n",
|
||
|
|
"@widgets.interact(key=widgets.RadioButtons(\n",
|
||
|
|
" options=key_list,\n",
|
||
|
|
"# value='pineapple', # Defaults to 'pineapple'\n",
|
||
|
|
"# layout={'width': 'max-content'}, # If the items' names are long\n",
|
||
|
|
" description='stimulus:',\n",
|
||
|
|
" disabled=False\n",
|
||
|
|
"))\n",
|
||
|
|
"\n",
|
||
|
|
"def PSTH_key(key):\n",
|
||
|
|
" if key=='pair':\n",
|
||
|
|
" @widgets.interact(phi=widgets.IntSlider(0, min=0, max=19), theta=widgets.IntSlider(0, min=0, max=19))\n",
|
||
|
|
" \n",
|
||
|
|
" def PSTH(phi, theta):\n",
|
||
|
|
" stim_id = theta*20 + phi\n",
|
||
|
|
"\n",
|
||
|
|
" # stim_id = int(np.floor(stim_id * num_stim[key]))\n",
|
||
|
|
" fig, ax = plt.subplots(1, 4, figsize=(5*4,1*4))\n",
|
||
|
|
" for unit_id in range(4):\n",
|
||
|
|
" ax[unit_id%4].plot(time, smooth_firing_rate[key][unit_id, stim_id])\n",
|
||
|
|
" ax[unit_id%4].plot(time, avg_firing_rate[key][unit_id, stim_id] * np.ones(len(time)))\n",
|
||
|
|
" ax[unit_id%4].plot(time, np.mean(avg_firing_rate['pair'][unit_id]) * np.ones(len(time)), 'g--')\n",
|
||
|
|
" ax[unit_id%4].plot(time, len(spike_times[unit_id])/experiment_dur * np.ones(len(time)), 'b--')\n",
|
||
|
|
"\n",
|
||
|
|
" if key == 'pair':\n",
|
||
|
|
" ax[unit_id%4].plot(stim_time, -1*np.ones(len(stim_time)), 'r-', label=' $\\\\theta = %d' %stim_val[key][stim_id][0]+'^\\circ$\\n $\\phi = %d' %stim_val[key][stim_id][1]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" ax[unit_id%4].plot(stim_time, -1*np.ones(len(stim_time)), 'r-', label='stimulus: '+key+' = $%d' %stim_val[key][stim_id]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id%4].set_ylabel('firing rate/Hz')\n",
|
||
|
|
" ax[unit_id%4].set_xlabel('t/ms')\n",
|
||
|
|
" ax[unit_id%4].set_ylim([-2,200])\n",
|
||
|
|
" ax[unit_id%4].set_title('PSTH ' + '%d'%stim_num_trial[key] + ' trials - unit '+'%d'%unit_id)\n",
|
||
|
|
" ax[unit_id%4].legend(loc='upper center')\n",
|
||
|
|
" plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" @widgets.interact(stim_id=widgets.IntSlider(0, min=0, max=19))\n",
|
||
|
|
" \n",
|
||
|
|
" def PSTH(stim_id):\n",
|
||
|
|
"\n",
|
||
|
|
" # stim_id = int(np.floor(stim_id * num_stim[key]))\n",
|
||
|
|
" fig, ax = plt.subplots(1, 4, figsize=(5*4,1*4))\n",
|
||
|
|
" for unit_id in range(4):\n",
|
||
|
|
" ax[unit_id%4].plot(time, smooth_firing_rate[key][unit_id, stim_id])\n",
|
||
|
|
" ax[unit_id%4].plot(time, avg_firing_rate[key][unit_id, stim_id] * np.ones(len(time)))\n",
|
||
|
|
" ax[unit_id%4].plot(time, np.mean(avg_firing_rate['pair'][unit_id]) * np.ones(len(time)), 'g--')\n",
|
||
|
|
" ax[unit_id%4].plot(time, len(spike_times[unit_id])/experiment_dur * np.ones(len(time)), 'b--')\n",
|
||
|
|
"\n",
|
||
|
|
" if key == 'pair':\n",
|
||
|
|
" ax[unit_id%4].plot(stim_time, -1*np.ones(len(stim_time)), 'r-', label=' $\\\\theta = %d' %stim_val[key][stim_id][0]+'^\\circ$\\n $\\phi = %d' %stim_val[key][stim_id][1]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" ax[unit_id%4].plot(stim_time, -1*np.ones(len(stim_time)), 'r-', label='stimulus: '+key+' = $%d' %stim_val[key][stim_id]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id%4].set_ylabel('firing rate/Hz')\n",
|
||
|
|
" ax[unit_id%4].set_xlabel('t/ms')\n",
|
||
|
|
" ax[unit_id%4].set_ylim([-2,200])\n",
|
||
|
|
" ax[unit_id%4].set_title('PSTH ' + '%d'%stim_num_trial[key] + ' trials - unit '+'%d'%unit_id)\n",
|
||
|
|
" ax[unit_id%4].legend(loc='upper center')\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 60,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "aff3a076e2af43c98ae29831b41297e9",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(RadioButtons(description='stimulus:', options=('pair', 'orientation', 'phase'), value='p…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# key = 'orientation'\n",
|
||
|
|
"time = (time_bin_edges[:-1] - delta_t_on)/time_bin\n",
|
||
|
|
"stim_time = np.arange(0, max_dur, time_bin)/time_bin\n",
|
||
|
|
"\n",
|
||
|
|
"@widgets.interact(stim_id=widgets.FloatSlider(0, min=0, max=1, step=0.01),\n",
|
||
|
|
" key=widgets.RadioButtons(\n",
|
||
|
|
" options=key_list,\n",
|
||
|
|
"# value='pineapple', # Defaults to 'pineapple'\n",
|
||
|
|
"# layout={'width': 'max-content'}, # If the items' names are long\n",
|
||
|
|
" description='stimulus:',\n",
|
||
|
|
" disabled=False\n",
|
||
|
|
"))\n",
|
||
|
|
"\n",
|
||
|
|
"def PSTH(key, stim_id):\n",
|
||
|
|
" stim_id = int(np.floor(stim_id * num_stim[key]))\n",
|
||
|
|
" fig, ax = plt.subplots(10, 4, figsize=(5*4,10*4))\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(time, smooth_firing_rate[key][unit_id, stim_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(time, avg_firing_rate[key][unit_id, stim_id] * np.ones(len(time)))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(time, np.mean(avg_firing_rate['pair'][unit_id]) * np.ones(len(time)), 'g--')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(time, len(spike_times[unit_id])/experiment_dur * np.ones(len(time)), 'b--')\n",
|
||
|
|
"\n",
|
||
|
|
" if key == 'pair':\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_time, -1*np.ones(len(stim_time)), 'r-', label=' $\\\\theta = %d' %stim_val[key][stim_id][0]+'^\\circ$\\n $\\phi = %d' %stim_val[key][stim_id][1]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_time, -1*np.ones(len(stim_time)), 'r-', label='stimulus: '+key+' = $%d' %stim_val[key][stim_id]+'^\\circ$')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('firing rate/Hz')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('t/ms')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylim([-2,200])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_title('PSTH ' + '%d'%stim_num_trial[key] + ' trials - unit '+'%d'%unit_id)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].legend(loc='upper center')\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Subtract average phase response"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"r_fphi = np.copy(spike_count_rate)\n",
|
||
|
|
"for stim_id, trials in enumerate(stim_id_trial['phase']):\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" r_fphi[unit_id, trials] -= avg_firing_rate['phase'][unit_id, stim_id]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[ 1.69541249e-03, 3.22163614e+01, 2.03681866e+00, ...,\n",
|
||
|
|
" 2.90363390e+00, -2.78436986e+01, 3.35952210e+01],\n",
|
||
|
|
" [ 6.81509867e+01, -2.28685128e+01, -2.80524644e+01, ...,\n",
|
||
|
|
" -2.65551073e+01, -2.28685128e+01, -2.37690514e+01],\n",
|
||
|
|
" [ 1.30971358e+01, -1.61529328e+01, -1.69339763e+01, ...,\n",
|
||
|
|
" -1.60046681e+01, 1.38770972e+01, -1.74139233e+01],\n",
|
||
|
|
" ...,\n",
|
||
|
|
" [-6.00600601e-02, -5.97908660e-02, -2.99401198e-02, ...,\n",
|
||
|
|
" 0.00000000e+00, -5.97908660e-02, 0.00000000e+00],\n",
|
||
|
|
" [-3.00300300e-02, 0.00000000e+00, 0.00000000e+00, ...,\n",
|
||
|
|
" 0.00000000e+00, 0.00000000e+00, -2.99401198e-02],\n",
|
||
|
|
" [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
|
||
|
|
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"r_fphi"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"theta = np.copy(stim['grat_orientation'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(20000,)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"theta.shape"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"C_r_fphi_theta = [np.convolve(r_fphi[unit_id], theta[::-1])[::-1] for unit_id in range(num_unit)] # reverse correlation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x24ae2bd0460>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(C_r_fphi_theta[0])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([13505, 24614, 24976, 28254, 27733, 13139, 14842, 13004, 13395,\n",
|
||
|
|
" 13337, 13460, 12405, 26205, 5522, 24844, 13343, 13603, 24607,\n",
|
||
|
|
" 5545, 27818, 24654, 4900, 12822, 23467, 32994, 24963, 4929,\n",
|
||
|
|
" 13537, 23406, 32700, 32950, 22641, 33750, 27543, 9353, 25907,\n",
|
||
|
|
" 27023, 25357, 20291, 33125], dtype=int64)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"np.argmax(C_r_fphi_theta, axis=1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"autocorr_theta = np.convolve(theta, theta[::-1])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x24ae22ba5b0>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(autocorr_theta)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"temporal_kernel = np.fft.ifft(np.fft.fft(C_r_fphi_theta)/np.fft.fft(autocorr_theta))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x24ae23229d0>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(np.real(temporal_kernel[1]))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([58.61796736, 46.68999397, 1.33161805, ..., 10.85536343,\n",
|
||
|
|
" 21.27754034, 37.21827067])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"avg_firing_rate['phase'][0, trial_stim_id['phase']] + np.real(np.convolve(temporal_kernel[0], theta, 'valid'))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([29.93842435, 27.84369865, 27.99321137, ..., 27.12639613,\n",
|
||
|
|
" 27.84369865, 26.46483905])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"avg_firing_rate['phase'][0, trial_stim_id['phase']]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([29.94011976, 60.06006006, 30.03003003, ..., 30.03003003,\n",
|
||
|
|
" 0. , 60.06006006])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"spike_count_rate[0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
"for unit_id in range(40):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(np.real(temporal_kernel[unit_id]))\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_ylabel('avg firing rate / Hz')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylim([-0.1,0.1])\n",
|
||
|
|
"\n",
|
||
|
|
"# ax[0,0].set_title('firing rate (Hz) vs. phase')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Perispike Stimulus Histogram"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"19\r"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"tau_bin = 0.05\n",
|
||
|
|
"tau_range = np.arange(0, 0.5+tau_bin, tau_bin)\n",
|
||
|
|
"theta_hist = np.zeros((num_unit, len(tau_range), num_stim['orientation']))\n",
|
||
|
|
"for stim_id, trials in enumerate(stim_id_trial['orientation']):\n",
|
||
|
|
" print(stim_id, end='\\r')\n",
|
||
|
|
" for trial_id in trials:\n",
|
||
|
|
" t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
|
||
|
|
" for i, tau in enumerate(tau_range):\n",
|
||
|
|
" # how many spikes between t_on + tau and t_off + tau\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" theta_hist[unit_id, i, stim_id] += np.sum((spike_times[unit_id] > t_on + tau) & (spike_times[unit_id] < t_off + tau))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10,4, figsize=(5*4, 10*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_val['orientation'], theta_hist[unit_id, 1])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"the range of phases should be restricted to get good orientaiton tuning curves (?)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"delay_orientation_tuning = theta_hist / 0.084"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"To carry out topological data analysis, we need 400 data points (number of stimulus pairs) in the neuronal firing rate space."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 40,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"19\r"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"key = 'phase'\n",
|
||
|
|
"tau_bin = 0.05\n",
|
||
|
|
"tau_range = np.arange(0, 0.5+tau_bin, tau_bin)\n",
|
||
|
|
"phase_hist = np.zeros((num_unit, len(tau_range), num_stim[key]))\n",
|
||
|
|
"for stim_id, trials in enumerate(stim_id_trial[key]):\n",
|
||
|
|
" print(stim_id, end='\\r')\n",
|
||
|
|
" for trial_id in trials:\n",
|
||
|
|
" t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
|
||
|
|
" for i, tau in enumerate(tau_range):\n",
|
||
|
|
" # how many spikes between t_on + tau and t_off + tau\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" phase_hist[unit_id, i, stim_id] += np.sum((spike_times[unit_id] > t_on + tau) & (spike_times[unit_id] < t_off + tau))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10,4, figsize=(5*4, 10*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_val['phase'], phase_hist[unit_id, 1])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 42,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"399\r"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"key = 'pair'\n",
|
||
|
|
"tau_bin = 0.05\n",
|
||
|
|
"tau_range = np.arange(0, 0.5+tau_bin, tau_bin)\n",
|
||
|
|
"pair_hist = np.zeros((num_unit, len(tau_range), num_stim[key]))\n",
|
||
|
|
"for stim_id, trials in enumerate(stim_id_trial[key]):\n",
|
||
|
|
" print(stim_id, end='\\r')\n",
|
||
|
|
" for trial_id in trials:\n",
|
||
|
|
" t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
|
||
|
|
" for i, tau in enumerate(tau_range):\n",
|
||
|
|
" # how many spikes between t_on + tau and t_off + tau\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" pair_hist[unit_id, i, stim_id] += np.sum((spike_times[unit_id] > t_on + tau) & (spike_times[unit_id] < t_off + tau))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"2D Tuning"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"avg_firing_rate_pair = pair_hist.reshape((num_unit, len(tau_range), num_stim['orientation'], num_stim['phase']))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "ValueError",
|
||
|
|
"evalue": "cannot reshape array of size 176000 into shape (40,400)",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[1;32mc:\\Users\\arash\\Documents\\Uni\\Research Project (Topology)\\staticgrating-phasefreq-topology\\01_Firing_Rate.ipynb Cell 53'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000052?line=0'>1</a>\u001b[0m \u001b[39m# normalize\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000052?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mscipy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mstats\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msts\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/01_Firing_Rate.ipynb#ch0000052?line=2'>3</a>\u001b[0m avg_firing_rate_pair_normalized \u001b[39m=\u001b[39m sts\u001b[39m.\u001b[39mzscore(avg_firing_rate_pair\u001b[39m.\u001b[39;49mreshape(num_unit, \u001b[39m20\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m20\u001b[39;49m), axis \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mreshape(num_unit, \u001b[39m20\u001b[39m, \u001b[39m20\u001b[39m)\n",
|
||
|
|
"\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 176000 into shape (40,400)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# normalize\n",
|
||
|
|
"import scipy.stats as sts\n",
|
||
|
|
"avg_firing_rate_pair_normalized = sts.zscore(avg_firing_rate_pair.reshape(num_unit, 20*20), axis = 1).reshape(num_unit, 20, 20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
"for unit_id in range(40):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].imshow(avg_firing_rate_pair[unit_id, 1], interpolation='gaussian')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticks(np.arange(20)[::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticklabels(stim_val['phase'][::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticks(np.arange(20)[::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 46,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "25e7381d29664ebcb827a4a2ac58391f",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='tau_id', max=10), Output()), _dom_classes=('widget-inter…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# klein bottle symmetry\n",
|
||
|
|
"@widgets.interact(tau_id=widgets.IntSlider(0, min=0, max=len(tau_range)-1))\n",
|
||
|
|
"\n",
|
||
|
|
"def klein_bottle(tau_id):\n",
|
||
|
|
" fig, ax = plt.subplots(1, 4, figsize=(5*4, 1*4))\n",
|
||
|
|
" klein_bottle_rate = np.zeros((num_unit, 40, 40))\n",
|
||
|
|
" for unit_id in range(4):\n",
|
||
|
|
" x = np.hstack((avg_firing_rate_pair[unit_id,tau_id], avg_firing_rate_pair[unit_id,tau_id]))\n",
|
||
|
|
" klein_bottle_rate[unit_id] = np.vstack((x, x[:,::-1]))\n",
|
||
|
|
" ax[unit_id%4].imshow(klein_bottle_rate[unit_id], interpolation='gaussian')\n",
|
||
|
|
" ax[unit_id%4].plot()\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_xticks(np.arange(40)[::5])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_xticklabels(np.concatenate((stim_val['phase'][::5],stim_val['phase'][::5])))\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_yticks(np.arange(40)[::5])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax[unit_id%4].set_xticks([])\n",
|
||
|
|
" ax[unit_id%4].set_yticks([])\n",
|
||
|
|
" ax[unit_id%4].plot(np.arange(40), 19.5*np.ones(40), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id%4].plot(19.5*np.ones(40), np.arange(40), 'r', linewidth=0.5)\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id%4].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Klein bottle"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 47,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "2e21faa75c3049a58ce37b8e661b0991",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='tau_id', max=10), Output()), _dom_classes=('widget-inter…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# klein bottle symmetry\n",
|
||
|
|
"@widgets.interact(tau_id=widgets.IntSlider(0, min=0, max=len(tau_range)-1))\n",
|
||
|
|
"\n",
|
||
|
|
"def klein_bottle(tau_id):\n",
|
||
|
|
" fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
" klein_bottle_rate = np.zeros((num_unit, 40, 40))\n",
|
||
|
|
" for unit_id in range(40):\n",
|
||
|
|
" x = np.hstack((avg_firing_rate_pair[unit_id,tau_id], avg_firing_rate_pair[unit_id,tau_id]))\n",
|
||
|
|
" klein_bottle_rate[unit_id] = np.vstack((x, x[:,::-1]))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].imshow(klein_bottle_rate[unit_id], interpolation='gaussian')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot()\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_xticks(np.arange(40)[::5])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_xticklabels(np.concatenate((stim_val['phase'][::5],stim_val['phase'][::5])))\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_yticks(np.arange(40)[::5])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticks([])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticks([])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(np.arange(40), 19.5*np.ones(40), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(19.5*np.ones(40), np.arange(40), 'r', linewidth=0.5)\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOpUlEQVR4nO3cf6jd9X3H8eeruTRrEUyi8UeN2bVVGHGDFg5K2QauaoyDNtL6h90fDVtL/lj9Y5VCUxzT2v6hbp2ltNsIbSEIa3SO0kApEm2FMYb1xDrarE1zjS0mVZuaIDipkvW9P+7X7Xg5Mffec+49OX6eDzjc8/1+P/fe98cLeeac742pKiRJ7XrbpAeQJE2WIZCkxhkCSWqcIZCkxhkCSWrczKQHWI7zzz+/ZmdnJz2GJE2VAwcO/LqqNi48P5UhmJ2dpd/vT3oMSZoqSX4x7LxvDUlS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS48YSgiTbkhxKMpdk15Dra5M80F1/PMnsguubk7yc5NPjmEeStHgjhyDJGuCrwI3AFuCjSbYsWPZx4GRVXQ7cB9yz4PrfA98ddRZJ0tKN4xXBVcBcVR2pqteAvcD2BWu2A3u65w8B1yYJQJKbgGeAg2OYRZK0ROMIwSXAswPHR7tzQ9dU1SngJeC8JOcAnwE+d6ZvkmRnkn6S/vHjx8cwtiQJJn+z+E7gvqp6+UwLq2p3VfWqqrdx48aVn0ySGjEzhq9xDLh04HhTd27YmqNJZoBzgReBq4Gbk9wLrAN+m+Q3VfWVMcwlSVqEcYTgCeCKJJcx/wf+LcCfLVizD9gB/AdwM/C9qirgj19fkORO4GUjIEmra+QQVNWpJLcCDwNrgG9U1cEkdwH9qtoHfB24P8kccIL5WEiSzgKZ/4v5dOn1etXv9yc9hiRNlSQHqqq38PykbxZLkibMEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS48YSgiTbkhxKMpdk15Dra5M80F1/PMlsd/76JAeS/Kj7+IFxzCNJWryRQ5BkDfBV4EZgC/DRJFsWLPs4cLKqLgfuA+7pzv8a+GBV/QGwA7h/1HkkSUszjlcEVwFzVXWkql4D9gLbF6zZDuzpnj8EXJskVfXDqvpld/4g8I4ka8cwkyRpkcYRgkuAZweOj3bnhq6pqlPAS8B5C9Z8BHiyql4dw0ySpEWamfQAAEmuZP7toq1vsmYnsBNg8+bNqzSZJL31jeMVwTHg0oHjTd25oWuSzADnAi92x5uAbwEfq6qnT/dNqmp3VfWqqrdx48YxjC1JgvGE4AngiiSXJXk7cAuwb8GafczfDAa4GfheVVWSdcB3gF1V9e9jmEWStEQjh6B7z/9W4GHgJ8CDVXUwyV1JPtQt+zpwXpI54Dbg9V8xvRW4HPibJE91jwtGnUmStHipqknPsGS9Xq/6/f6kx5CkqZLkQFX1Fp73XxZLUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIUuPGEoIk25IcSjKXZNeQ62uTPNBdfzzJ7MC1z3bnDyW5YRzzSJIWb+QQJFkDfBW4EdgCfDTJlgXLPg6crKrLgfuAe7rP3QLcAlwJbAP+oft6kqRVMo5XBFcBc1V1pKpeA/YC2xes2Q7s6Z4/BFybJN35vVX1alU9A8x1X0+StErGEYJLgGcHjo9254auqapTwEvAeYv8XACS7EzST9I/fvz4GMaWJMEU3Syuqt1V1auq3saNGyc9jiS9ZYwjBMeASweON3Xnhq5JMgOcC7y4yM+VJK2gcYTgCeCKJJcleTvzN3/3LVizD9jRPb8Z+F5VVXf+lu63ii4DrgB+MIaZJEmLNDPqF6iqU0luBR4G1gDfqKqDSe4C+lW1D/g6cH+SOeAE87GgW/cg8F/AKeCTVfU/o84kSVq8zP/FfLr0er3q9/uTHkOSpkqSA1XVW3h+am4WS5JWhiGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMaNFIIkG5LsT3K4+7j+NOt2dGsOJ9nRnXtnku8k+WmSg0nuHmUWSdLyjPqKYBfwaFVdATzaHb9Bkg3AHcDVwFXAHQPB+Luq+j3gfcAfJrlxxHkkSUs0agi2A3u653uAm4asuQHYX1UnquoksB/YVlWvVNX3AarqNeBJYNOI80iSlmjUEFxYVc91z58HLhyy5hLg2YHjo925/5NkHfBB5l9VSJJW0cyZFiR5BLhoyKXbBw+qqpLUUgdIMgN8E/hyVR15k3U7gZ0AmzdvXuq3kSSdxhlDUFXXne5akheSXFxVzyW5GPjVkGXHgGsGjjcBjw0c7wYOV9WXzjDH7m4tvV5vycGRJA036ltD+4Ad3fMdwLeHrHkY2JpkfXeTeGt3jiRfAM4F/mrEOSRJyzRqCO4Grk9yGLiuOyZJL8nXAKrqBPB54InucVdVnUiyifm3l7YATyZ5KsknRpxHkrREqZq+d1l6vV71+/1JjyFJUyXJgarqLTzvvyyWpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMaNFIIkG5LsT3K4+7j+NOt2dGsOJ9kx5Pq+JD8eZRZJ0vKM+opgF/BoVV0BPNodv0GSDcAdwNXAVcAdg8FI8mHg5RHnkCQt06gh2A7s6Z7vAW4asuYGYH9Vnaiqk8B+YBtAknOA24AvjDiHJGmZRg3BhVX1XPf8eeDCIWsuAZ4dOD7anQP4PPBF4JUzfaMkO5P0k/SPHz8+wsiSpEEzZ1qQ5BHgoiGXbh88qKpKUov9xkneC7ynqj6VZPZM66tqN7AboNfrLfr7SJLe3BlDUFXXne5akheSXFxVzyW5GPjVkGXHgGsGjjcBjwHvB3pJft7NcUGSx6rqGiRJq2bUt4b2Aa//FtAO4NtD1jwMbE2yvrtJvBV4uKr+sareVVWzwB8BPzMCkrT6Rg3B3cD1SQ4D13XHJOkl+RpAVZ1g/l7AE93jru6cJOkskKrpe7u91+tVv9+f9BiSNFWSHKiq3sLz/stiSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxqWqJj3DkiU5Dvxi0nMs0fnAryc9xCpzz21wz9Pjd6tq48KTUxmCaZSkX1W9Sc+xmtxzG9zz9POtIUlqnCGQpMYZgtWze9IDTIB7boN7nnLeI5CkxvmKQJIaZwgkqXGGYIySbEiyP8nh7uP606zb0a05nGTHkOv7kvx45Sce3Sh7TvLOJN9J8tMkB5PcvbrTL02SbUkOJZlLsmvI9bVJHuiuP55kduDaZ7vzh5LcsKqDj2C5e05yfZI
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot()\n",
|
||
|
|
"for trial_id in range(num_trial):\n",
|
||
|
|
" t_on, t_off = stim['stim_ontime'][trial_id]//time_bin, stim['stim_offtime'][trial_id]//time_bin\n",
|
||
|
|
" if t_off > 0 and t_on < 1:\n",
|
||
|
|
" stim_time = np.arange(t_on, t_off)\n",
|
||
|
|
" plt.plot(stim_time, stim_trial[trial_id]*np.ones(len(stim_time)), c=cmap(stim_trial[trial_id]))\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# save\n",
|
||
|
|
"\n",
|
||
|
|
"spike_data = {'spike_count_rate':spike_count_rate, 'avg_firing_rate': avg_firing_rate, 'sem_firing_rate':sem_firing_rate,\n",
|
||
|
|
"'firing_rate': firing_rate, 'stim_num_trial':stim_num_trial, 'C_r_fphi_theta':C_r_fphi_theta, 'theta_hist':theta_hist, 'phase_hist':phase_hist, 'pair_hist':pair_hist}\n",
|
||
|
|
"\n",
|
||
|
|
"file = open('spike_data.pkl', 'wb')\n",
|
||
|
|
"pickle.dump(spike_data, file)\n",
|
||
|
|
"file.close()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3.10.5 64-bit",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.5"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4,
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|